Introduction to data science in R

Introduction to data visualization

Brian S. Evans, Ph.D.
Migratory Bird Center
Smithsonian Conservation Biology Institute

Setup for the lesson

# Load tidyverse library:

library(tidyverse)

Setup for the lesson

The function paste0 is used to paste two string values together. For example, we can paste the values 'hello' and 'World' together as follows. The resultant object is a nice looking camel case value.

# Load RCurl library:

paste0('hello', 'World')

## [1] "helloWorld"

Setup for the lesson

We will use the function paste0 to create an easy-to-read url

gitUrl <-
  'https://raw.githubusercontent.com/bsevansunc/'

courseData <-
  'smsc_data_science/master/data/'

paste0(
  gitUrl,
  courseData,
  'birdMeasures.csv')

## [1] "https://raw.githubusercontent.com/bsevansunc/smsc_data_science/master/data/birdMeasures.csv"

Setup for the lesson

We can use this url to read in the data.

birdMeasures <-
  read.csv(
    paste0(
      gitUrl,
      courseData,
      'birdMeasures.csv'))

Take a moment to explore the data frame birdMeasures

Setup for the lesson

Let’s read in birdMeasures as a tibble, because there are a lot of data in that file!

birdMeasures <-
  dplyr::as_tibble(
    read.csv(
      paste0(
        gitUrl,
        courseData,
        'birdMeasures.csv')))

Setup for the lesson

I don’t like that there are a lot of factors in the data frame. How might we overcome this?

birdMeasures

## # A tibble: 5,234 x 11
##    id    region spp   bandNumber enc   date   mass  wing    tl age   sex  
##    <fct> <fct>  <fct> <fct>      <fct> <fct> <dbl> <dbl> <dbl> <fct> <fct>
##  1 g435… Atlan… NOCA  2641-63316 B     2014…  36.7    92   100 AHY   M    
##  2 c703… Atlan… NOCA  2641-63362 B     2014…  40.4    93    98 SY    M    
##  3 b264… Atlan… CACH  2710-53995 B     2015…   9.7    60    50 AHY   M    
##  4 y107… Atlan… AMRO  1352-27606 B     2015…  80.1   130    97 AHY   F    
##  5 w113… Atlan… AMRO  1352-27609 B     2015…  73.8   130    96 AHY   M    
##  6 f364… Atlan… NOCA  2641-63899 B     2015…  42.1    86   100 AHY   F    
##  7 m960… Atlan… NOCA  2641-63900 B     2015…  42.7    92   102 AHY   F    
##  8 e424… Atlan… AMRO  1352-27610 B     2015…  72.7   130    97 AHY   F    
##  9 k126… Atlan… AMRO  1352-27614 B     2015…  75     120    87 AHY   F    
## 10 j492… Atlan… GRCA  2657-47401 B     2015…  38.3    87    90 AHY   M    
## # ... with 5,224 more rows

Setup for the lesson

I don’t like that there are a lot of factors in the data frame. How might we overcome this?

birdMeasures <-
  dplyr::as_tibble(
    read.csv(
      paste0(
        gitUrl,
        courseData,
        'birdMeasures.csv'),
      stringsAsFactors = FALSE))

Our goal

Initiating a plot

ggplot(data = birdMeasures)

Aesthetics

Aesthetics describe mapping the value of some variable to an observable feature.

ggplot(
  data = birdMeasures, 
  mapping = aes(x = spp))

Geometries

A geometry plot element provides a visible representation of observations. They are called using the function geom_[geometry]. Geometries are frequently used include:

geom_bar: Bars for bar plots
geom_histogram: Histogram plot for observing distributions
geom_density: Density plot for observing distributions
geom_point: Point plot for observing raw data

Geometries

ggplot(
  data = birdMeasures, 
  mapping = aes(x = spp)) +
  geom_bar()

Geometries

The data = and mapping = were not really necessary. Let’s simplify:

ggplot(
  birdMeasures, 
  aes(x = spp)) +
  geom_bar()

Exercise One:

The function geom_density can be used to display the density distribution of a vector. Using the aesthetic x = mass, display the distribution of Black-capped chickadee (BCCH) and Carolina chickadee (CACH) mass measurements:

Exercise One:

# Subset birdCounts to BCCH and CACH and plot density:

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) + 
  geom_density()

Exercise One:

Geometries: Adding arguments

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram()

Geometries: Adding arguments

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(binwidth = 1)

Geometries: Adding arguments

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(bins = 20)

Geometries: Adding arguments

ggplot(
  birdMeasures[birdMeasures$spp != 'NOCA',],
  aes(x = spp)) +
  geom_bar(fill = 'gray')

Geometries: Adding arguments

ggplot(
  birdMeasures[birdMeasures$spp != 'NOCA',],
  aes(x = spp)) +
  geom_bar(fill = 'gray', 
           color = 'black')

Geometries: Adding arguments

ggplot(
  birdMeasures[birdMeasures$spp != 'NOCA',],
  aes(x = spp)) +
  geom_bar(fill = 'gray', 
           color = 'black',
           size = 0.7)

Exercise Two:

Modify your density plot from Exercise One:

Use the fill argument to fill your density shape with the color “gray”:
The argument alpha can be applied to a geometry to adjust its transparency. Adjust the density shape to alpha = 0.7

Exercise Two:

# Subset birdCounts to BCCH and CACH and plot density:

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_density(fill = 'gray', alpha = 0.7)

Exercise Two:

Geometries: Adding aesthetics

Aesthetics describe mapping the value of some variable to an observable feature.

ggplot(
  birdMeasures[birdMeasures$spp != 'NOCA',],
  aes(x = spp)) +
  geom_bar(aes(fill = region))

Geometries: Adding aesthetics

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex),
    bins = 20)

Geometries: Adding aesthetics

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex),
    bins = 20,
    color = 'black')

Exercise Three:

Modify your density plot from Exercise One. Use the fill argument of the function geom_density to assign a different fill color to females and males.

Exercise Three:

# Subset birdCounts to BCCH and CACH and plot density:

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_density(
    aes(fill = sex),
    alpha = 0.7)

Exercise Three:

Facets

Faceting splits plots, by some variable, into multiple plots.

Facets

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex), 
    bins = 20,
    color = 'black') +
  facet_wrap(~spp)

Facets

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex), 
    bins = 20,
    color = 'black') +
  facet_wrap(~spp, nrow = 2)

Exercise Four:

Modify your density plot from Exercise Three. Use the facet_wrap function with the argument nrow = 2 to generate separate plots of Black-capped and Carolina chickadees.

Exercise Four:

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_density(
    aes(fill = sex),
    alpha = 0.7) +
  facet_wrap(~spp, nrow = 2)

Exercise Four:

Labels

Labels describes the plot and axis titles.

Labels

ggplot(
  birdMeasures[birdMeasures$spp != 'NOCA',],
  aes(x = spp)) +
  geom_bar(
    aes(fill = region),
    color = 'black',
    size = .7) +
  labs(
    title = 'Birds banded and recaptured 2000-2017',
    x = 'Species',
    y = 'Count')

Labels

Exercise Five:

Modify the density plot you created in Exercise Four:

Add the title “Mass of Carolina and Black-capped chickadees and capitalize the x and y axis titles

Colors

The default colors of ggplot are pretty ugly. Luckily you can modify in an infinite number of ways!

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex), 
    bins = 20,
    color = 'black') +
  facet_wrap(~spp, nrow = 2) +
  scale_fill_manual(values = c('blue', 'red'))

Colors

Color-picker apps can be a great way to find colors that you like on the internet.

Colors

Using Team Zissou’s hat and shirt color:

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex), 
    bins = 20,
    color = 'black') +
  facet_wrap(~spp, nrow = 2) +
  scale_fill_manual(values = c('#9EB8C5', '#F32017'))

Colors

You can hunt around to find colors that you like and then save your palette for use later:

zPalette <- c('#9EB8C5', '#F32017')

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex), 
    bins = 20,
    color = 'black') +
  facet_wrap(~spp, nrow = 2) +
  scale_fill_manual(values = zPalette)

Colors

Exercise Six:

Modify the density plot you created in Exercise Five. Use scale_fill_manual to set custom fill colors.

Legends

We can use the scale_fill_manual function from above to modify the legend by specifying the name and label attributes:

ggplot(
  birdMeasures[birdMeasures$spp %in% c('BCCH', 'CACH'),],
  aes(x = mass)) +
  geom_histogram(
    aes(fill = sex), 
    bins = 20,
    color = 'black') +
  facet_wrap(~spp, nrow = 2) +
  scale_fill_manual(values = c('#9EB8C5', '#F32017'), 
                    name = 'Sex', 
                    labels = c('Female', 'Male'))

Legends

Exercise Seven:

Modify the density plot you created in Exercise Six. Use scale_fill_manual to set the legend title and labels.